We present a decentralized adaptive filtering algorithm where each agent acts selfishly to maximize its payoff. Agents are only aware of the actions of other agents within their coalitions and have no knowledge of the actions of agents outside the coalition. We show that the global behavior of the system converges to the set of correlated equilibria. Thus simple behavior by individual agents can result in sophisticated global behavior.

In this paper, we present a strategic bidding framework for repeated auctions with monitoring and entry fees. We motivate and formally define the desired properties of our framework and present a recursive bidding algorithm, according to which buyers learn to avoid submitting bids in stages where they have a relatively low chance of winning the auctioned item. The proposed bidding strategies are computationally simple as players do not need to recompute the sequential strategies from the data collected to date. Pursuing the proposed efficient bidding (EB) algorithm, players monitor their relative performance in the course of the game and submit their bids based on their current estimate of the market condition. We prove the stability and robustness of the proposed strategies and show that they dominate myopic and random bidding strategies using an experiment in search engine marketing.

We analyze rational strategies of users in a dynamic grid market. We consider efficient usage of the shared resources in modeling users' preference relations, an objective that prevents congestion and consequently the collapse of the grid system. A repeated auction-based allocation protocol is presented for sharing the computational grid resources. We present a utility-maximizing bidding algorithm and illustrate the transient and long-term attitudes of users in an equilibrium of the dynamic resource-allocation game. It is shown that efficient bidding improves the long-term profits of the grid users.

We present two resource-allocation mechanisms for on-demand computing services in parallel and distributed systems, where users pay for their actual usage of the computational resources. We specialize our solution for allocation of grid resources which is a challenging issue due to the dynamic behavior of the system. The problem is studied from the seller's point of view and adjustment of the market capacity is proposed as his strategic move to maximize his profit from resource sharing.

Game theory deals with decision-making processes involving two or more parties with partly or completely conflicting interests. The players involved in the game usually make their decisions under conditions of risk or uncertainty. In this paper, an idea of nondeterministic payoffs is proposed and optimization is done in a more realistic fuzzy environment, helping each player describe his goal functions by using the linguistic variables. Since fuzzy numbers represent uncertain numeric values, their substitution for traditional crisp payoff values is described and their application is surveyed for a specific sample of contributive games - the game of civic duty. A closed formulation is developed for a vast general class of vague payoffs

A game is a decision making situation in which each player attempts to act in such a way that the game's circumstances get close to what desirable for him. To reach this goal, a player needs to have a suitable estimation of the other players' decisions. In this paper, we propose a fuzzy approach by which a player can attain an estimation of the other players' actions and pursue a strategy which maximizes its performance. Finally, the results of applying the proposed approach to the well-known "prisoner's dilemma" problem are brought to show the effectiveness of the method